IEEE Transactions on Reliability Review Policy

IEEE Transactions on Reliability

Special Section on Trustworthy AI for Autonomous Driving


Call for Papers


Background


Autonomous driving technologies have made phenomenal progress and shown significant societal and economic impacts. Recent breakthroughs in artificial intelligence (AI), especially deep learning technologies, demonstrate their importance in developing autonomous driving systems. However, due to the limited understanding of the underlying deep learning theories and the vulnerability of the technologies to out-of-distribution and adversarial scenarios, there is an increasing concern if decisions made by the systems can be trusted in life-critical use cases. Moreover, new AI approaches for autonomous driving introduces new challenges for software system verification of reliability, security, and safety.


To address the challenges, IEEE Transactions on Reliability will have a special section soliciting original work that makes novel theoretical or practical contributions to enable trustworthy AI for autonomous driving. Specifically, through the lens of trustworthy computing, trustworthy AI concerns the reliability, security, and safety of AI software systems for autonomous driving. In addition, systems should be extended with properties such as fairness, robustness, interpretability, and more. Furthermore, a whole new set of verification techniques should be invented to handle novel artifacts. Submissions will be reviewed and selected based on innovation, technical correctness, presentation, and practical relevance.

Topics


We welcome submissions in, but not limited to, the following topic areas:


  • Reliability, security, and safety of autonomous driving systems
  • Trustworthy AI software or system architecture
  • Formal methods for verification with extensions of fairness, robustness, and interpretability
  • Explainable, transparent, interpretable, verifiable, and certifiable artificial intelligence systems
  • Advice-taking and advisable autonomous driving systems
  • Privacy-preserving and responsible artificial intelligence
  • Data and model governance
  • Dataset bias
  • Out-of-distribution detection
  • Adversarial attack and defense
  • Deep traffic scene understanding and comprehension
  • Risk assessment and risk-aware decision making in traffic scenes
  • Uncertainty modeling and runtime verification

Submission Information


We welcome high-quality submissions that are original work, not published, and not currently submitted elsewhere. We also encourage extensions to conference papers, unless prohibited by copyright, if there is a significant difference in technical content. Improvements such as adding a new case study or including a description of additional related studies do not satisfy this requirement. A description explaining the differences between the conference paper and the journal submission is required. The overlap between each submission and other articles, including the authors' own papers and dissertations, should be less than 30%.


All submitted papers will undergo a rigorous peer-review process and must conform to the double-column, the single-spaced format of printed articles in the IEEE Transactions on Reliability with all figures and tables embedded in the paper, rather than listed at the end or in the appendix. Refer to the special guidelines posted at https://mc.manuscriptcentral.com/tr-ieee.

Important Dates


  • Submission Deadline:
  • 05/31/2025, rolling review and publication
  • Publication Date:
  • Rolling

    Editor-in-Chief


    • Professor Winston Shieh
      Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan
      Email: shiuhpyng@ieee.org

    Guest Editors


    • Professor Yi-Ting Chen
      Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan
      Email: ychen@cs.nycu.edu.tw
    • Professor Jinkyu Kim
      Department of Computer Science and Engineering, Korea University, Korea
      Email: jinkyukim@korea.ac.kr
    • Professor Ding Zhao
      Department of Mechanical Engineering, Carnegie Mellon University, USA
      Email: dingzhao@andrew.cmu.edu
    • Doctor Olaf Op den Camp
      Netherlands Organisation for Applied Scientific Research, Netherlands
      Email: olaf.opdencamp@tno.nl